Amazing Feature EngineeringFeature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
Stars: ✭ 218 (-58.56%)
Machine Learning Workflow With PythonThis is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation
Stars: ✭ 157 (-70.15%)
Kaggle CompetitionsThere are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.
Stars: ✭ 86 (-83.65%)
NlpythonThis repository contains the code related to Natural Language Processing using python scripting language. All the codes are related to my book entitled "Python Natural Language Processing"
Stars: ✭ 265 (-49.62%)
Drugs Recommendation Using ReviewsAnalyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.
Stars: ✭ 35 (-93.35%)
DeltapyDeltaPy - Tabular Data Augmentation (by @firmai)
Stars: ✭ 344 (-34.6%)
FeatexpFeature exploration for supervised learning
Stars: ✭ 688 (+30.8%)
Awesome Ai BooksSome awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning
Stars: ✭ 855 (+62.55%)
HeliomlA book about machine learning, statistics, and data mining for heliophysics
Stars: ✭ 36 (-93.16%)
Cookbook 2nd CodeCode of the IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018 [read-only repository]
Stars: ✭ 541 (+2.85%)
PyradiomicsOpen-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics
Stars: ✭ 563 (+7.03%)
Cookbook 2ndIPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018
Stars: ✭ 704 (+33.84%)
GspanPython implementation of frequent subgraph mining algorithm gSpan. Directed graphs are supported.
Stars: ✭ 103 (-80.42%)
ImageclassificationDeep Learning: Image classification, feature visualization and transfer learning with Keras
Stars: ✭ 83 (-84.22%)
GendisContains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.
Stars: ✭ 59 (-88.78%)
Lab WorkshopsMaterials for workshops on text mining, machine learning, and data visualization
Stars: ✭ 112 (-78.71%)
Gwu data miningMaterials for GWU DNSC 6279 and DNSC 6290.
Stars: ✭ 217 (-58.75%)
LasioPython library for reading and writing well data using Log ASCII Standard (LAS) files
Stars: ✭ 234 (-55.51%)
Interpretable machine learning with pythonExamples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Stars: ✭ 530 (+0.76%)
Php MlPHP-ML - Machine Learning library for PHP
Stars: ✭ 7,900 (+1401.9%)
TsfreshAutomatic extraction of relevant features from time series:
Stars: ✭ 6,077 (+1055.32%)
TsfelAn intuitive library to extract features from time series
Stars: ✭ 202 (-61.6%)
TfidfSimple TF IDF Library
Stars: ✭ 6 (-98.86%)
NniAn open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Stars: ✭ 10,698 (+1933.84%)
DataCon🏆DataCon大数据安全分析大赛,2019年方向二(恶意代码检测)冠军源码、2020年方向五(恶意代码分析)季军源码
Stars: ✭ 69 (-86.88%)
autoencoders tensorflowAutomatic feature engineering using deep learning and Bayesian inference using TensorFlow.
Stars: ✭ 66 (-87.45%)
fastknnFast k-Nearest Neighbors Classifier for Large Datasets
Stars: ✭ 64 (-87.83%)
Datasist A Python library for easy data analysis, visualization, exploration and modeling
Stars: ✭ 123 (-76.62%)
BlurrData transformations for the ML era
Stars: ✭ 96 (-81.75%)
AutofeatLinear Prediction Model with Automated Feature Engineering and Selection Capabilities
Stars: ✭ 178 (-66.16%)
Data Science Resources👨🏽🏫You can learn about what data science is and why it's important in today's modern world. Are you interested in data science?🔋
Stars: ✭ 171 (-67.49%)
50-days-of-Statistics-for-Data-ScienceThis repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.
Stars: ✭ 19 (-96.39%)
tsflexFlexible time series feature extraction & processing
Stars: ✭ 252 (-52.09%)
feature engineFeature engineering package with sklearn like functionality
Stars: ✭ 758 (+44.11%)
Pydataroadopen source for wechat-official-account (ID: PyDataLab)
Stars: ✭ 302 (-42.59%)
Open source demosA collection of demos showcasing automated feature engineering and machine learning in diverse use cases
Stars: ✭ 391 (-25.67%)
mistqlA miniature lisp-like language for querying JSON-like structures. Tuned for clientside ML feature extraction.
Stars: ✭ 260 (-50.57%)
Awesome Feature EngineeringA curated list of resources dedicated to Feature Engineering Techniques for Machine Learning
Stars: ✭ 433 (-17.68%)
gan tensorflowAutomatic feature engineering using Generative Adversarial Networks using TensorFlow.
Stars: ✭ 48 (-90.87%)
Feature SelectionFeatures selector based on the self selected-algorithm, loss function and validation method
Stars: ✭ 534 (+1.52%)
ProtrComprehensive toolkit for generating various numerical features of protein sequences
Stars: ✭ 30 (-94.3%)
Fantasy Basketball Scraping statistics, predicting NBA player performance with neural networks and boosting algorithms, and optimising lineups for Draft Kings with genetic algorithm. Capstone Project for Machine Learning Engineer Nanodegree by Udacity.
Stars: ✭ 146 (-72.24%)
featurewizUse advanced feature engineering strategies and select best features from your data set with a single line of code.
Stars: ✭ 229 (-56.46%)
Mli ResourcesH2O.ai Machine Learning Interpretability Resources
Stars: ✭ 428 (-18.63%)
Rong360用户贷款风险预测
Stars: ✭ 489 (-7.03%)